I assume it's because there are some very complex relationships and patterns that cannot be captured by decision trees. Tree models work better on simpler data at least that is my gut feeling based on previous experiments with similar data.
Interesting. Usually I have better luck with xgboost for tabular data, even when the relationships are complex (which usually means deeper trees). It does fall flat a lot of the time for very high dimensions, though. All data is different, I guess.
